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Xingliang Wang

2 accepted papers

2026

Beyond ReAct: A Planner-Centric Framework for Complex Tool-Augmented LLM Reasoning

AAAI 2026technical

Existing tool-augmented large language models (LLMs) encounter significant challenges when processing complex queries. Current frameworks such as ReAct are prone to local optimization traps due to their reliance on incremental decision-making processes. To address these limitations, we propose a nov

Cited by 0SourcePDFScholar
2025

RAG4GFM: Bridging Knowledge Gaps in Graph Foundation Models through Graph Retrieval Augmented Generation

NeurIPS 2025oral

Graph Foundation Models (GFMs) have demonstrated remarkable potential across graph learning tasks but face significant challenges in knowledge updating and reasoning faithfulness. To address these issues, we introduce the Retrieval-Augmented Generation (RAG) paradigm for GFMs, which leverages graph…

Cited by 0SourcecodeScholar